Architecting for HIPAA Security and Compliance on Amazon Web Services

whitePaper | March 12, 2020

In recent months, my company (Baron Consulting) has been proactively involved in setting up Data Collection Systems for a range of Private and Public Organisations we are servicing. Some of the data collection challenges have already been discussed in our recent Raw Data Collection 2020: Principles and Challenges White Paper. While the RDC (Raw Data Collection) paper analysed the current state of the everchanging Data Collection requirements, it did not have the scope to address technicalities of the RDC processes along with the specific Data Collection tools and methods. The purpose of this paper is to fill the void by looking into implementation of the automated data collection processes.

whitePaper | December 27, 2022

In this whitepaper we will define “data fabric,” look at different implementation strategies, and highlight the value of a logical approach. The paper will delve more deeply into the technical architecture of a logical data fabric and the key components to build a successful one, including the role played by artificial intelligence (AI) and data virtualization.

whitePaper | September 23, 2022

The COVID-19 pandemic times will go down in the annals of history as the ones which saw organizations face significant disruption in the way they executed their frontend business and backend operations. The pandemic waves have made organizations do the unthinkable – allowing their workforce to either operate exclusively from their homes, or at a minimum, distribute their working hours between home and office.

whitePaper | November 30, 2022

Modern cloud analytics can help financial services organisations to gain greater insight into their ever-increasing volumes of data, enabling them to improve customer experience, boost productivity and generate new revenue streams.

whitePaper | June 24, 2022

The reactive approach to discovering, analyzing, and correcting data quality issues in business applications has been marginally effective throughout the era of data consolidation for analytics (data warehousing, etc.) and the subsequent onset of “big data.”

whitePaper | February 19, 2020

Artificial Intelligence is developing fast. It will change our lives by improving healthcare (e.g. making diagnosis more precise, enabling better prevention of diseases), increasing the efficiency of farming, contributing to climate change mitigation and adaptation, improving the efficiency of production systems through predictive maintenance, increasing the security of Europeans, and in many other ways that we can only begin to imagine. At the same time, Artificial Intelligence (AI) entails a number of potential risks, such as opaque decision-making, gender-based or other kinds of discrimination, intrusion in our private lives or being used for criminal purposes.